Today, in project management, the focus of analysis and control is on the ability to estimate and associate what is effectively remembered as important with a given project. In other words, since seventy percent of all projects fail based on their original budget or finish date, it is clear that current systems struggle with successful estimations for outcomes. Part of this failure to predict, analyze and control project outcome stems from the inability to effectively mine and place into the proper context the avalanche of the data that could positively improve the predictive outcome of the project.
Project management, search software, data mining software and statistical/analytical tools could be used resolve project management shortfalls. However, these various tools exist in their own silos and are thereby not associated in a meaningful and usable manner. This failure is exacerbated as the complexity of projects increases as technology and society evolve.
Moreover, the concept of a project for many human endeavors is becoming widespread and mutating so that increasingly sophisticated tools, if applied correctly, could be implemented in more wide-ranging environments. For example, tools could be used in different ways, depending on the wide range of possibilities of what constitutes “a project”, and who is the “project manager”. For example, an individual planning threatening activities could be a deemed a “project manager” in the same way a more traditional individual, such as a certified project engineer, could plan a construction, research or information technology project. Other environments that rely heavily on project management and control and that could benefit from a more sophisticated analytical approach to project management include but are not limited to the film industry, the automotive industry, advertising, drug/pharmaceutical research, clinical medical trials, to name a few.
A need therefore exists in the art for a predictive analytic system and method that employs the best available software tools and that run on standard computer hardware in order to provide project predictive analytics to the end user.